Estimation of Power Spectral Density in SVPWM based Induction Motor Drives
V. Ananthalakshmi1,Y. Rama Mohan2, G. Sateesh3, T. Bramhananda Reddy4, A. Pradeep Kumar Yadav5
1V. Ananthalakshmi, Associate Professor, Department of EEE, Jawaharlal Nehru Technological University Hyderabad (Telangana), India.
2Y. Rama Mohan, Associate Professor, Department of EEE, Jawaharlal Nehru Technological University Hyderabad (Telangana), India.
3G. Sateesh, Associate Professor, Department of EEE, Jawaharlal Nehru Technological University Hyderabad (Telangana), India.
4T. Bramhananda Reddy, Associate Professor, Department of EEE, Jawaharlal Nehru Technological University Hyderabad (Telangana), India.
5A. Pradeep Kumar Yadav, Associate Professor, Department of EEE, Jawaharlal Nehru Technological University Hyderabad (Telangana), India.
Manuscript received on 20 August 2019 | Revised Manuscript received on 27 August 2019 | Manuscript Published on 31 August 2019 | PP: 602-605 | Volume-8 Issue-9S2 August 2019 | Retrieval Number: I11230789S219/19©BEIESP DOI: 10.35940/ijitee.I1123.0789S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper is readied the product programming of the SVPWM and half of breed PWM basically based DTC of recognition engine manipulate for assessing the strength Spectral Density (PSD) and the overall consonant mutilation (THD) of the road flows. The PWM set of guidelines utilizes three beautiful PWM methodologies like traditional SVPWM, AZPWM3 and combination PWM for the evaluation of the vitality spectra and consonant spectra. In quality spectra appraisal the extents of the power accrued at express frequencies and inside the consonant spectra the problem band sizes at one among a type replacing frequencies are taken into consideration for the assessment. To confirm the PWM calculations, numerical activity is performed making use of MATLAB/ simulink Telugu is one of the Dravidian languages which is morphologically rich. As in the other languages it too contains polysemous words which have different meanings in different contexts. There are several language models exist to solve the word sense disambiguation problem with respect to each language like English, Chinese, Hindi and Kannada etc. The proposed method gives a solution for the word sense disambiguation problem with the help of n-gram technique which has given good results in many other languages. The methodology mentioned in this paper finds the co-occurrence words of target polysemous word and we call them as n-grams. A Telugu corpus sent as input for training phase to find n-gram joint probabilities. By considering these joint probabilities the target polysemous word will be assigned a correct sense in testing phase. We evaluate the proposed method on some polysemous Telugu nouns and verbs. The methodology proposed gives the F-measure 0.94 when tested on Telugu corpus collected from CIIL, various news papers and story books.The present methodology can give better results with increase in size of training corpus and in future we plan to evaluate it on all words not only nouns and verbs.
Keywords: HPWM, Power Spectral Density, Total Harmonic Distortion
Scope of the Article: Performance Evaluation of Networks